{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 土炮自製撲克牌圖像資料集 (Home-made Poker cards Dataset)\n",
    "\n",
    "![](https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTRNL-d_M_vqDFx7n8eTwq5dBk0ulDVeDQFSPDh4PkSe3M4IJPK)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 資料集說明\n",
    "\n",
    "這個範例主要是希望透過一個簡單的示範來帶領大家使用一些簡單的工具與手法來產生一個可以用來作圖像辨識練習的圖像資料集。\n",
    "\n",
    "撲克牌圖像資料集主要由一張包含了52張撲克牌的大圖像來進行切割以及透過簡單的影像處理所產生出來。它包含52張撲克牌的圖像。\n",
    "主要的步驟如下:\n",
    "1. 下載原始撲克牌的大圖像, 例如: http://www.eypo-bet.com/wp-content/uploads/2015/12/poker.cards_.png\n",
    "2. 使用Windows內附的\"小畫家\"打開撲克牌的大圖像\n",
    "3. 使用Windows內附的\"裁剪工作(snipping tool)\"將每張牌一張一張的裁剪下來並存成png的格式\n",
    "4. 由於撲克牌有四種花樣與13個數字,存檔時以`{花樣}{數字}.png`的檔名進行儲存, 比如`c01.png` (黑桃1)\n",
    "5. 使用\"GIMP\"修圖軟體去除四週邊框\n",
    "6. 使用\"Excel\"編寫一個資料表來標註每一個圖像與標籤的關聯資訊:\n",
    "    * 花樣 (card_type): club, diamond, heart與spear\n",
    "    * 牌面數字(card_num): 1 ~ 13\n",
    "    * 標籤(card_label): club1~club13, diamond1~diamond13, heart1~heart13 與 spear1~spear13"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 資料準備\n",
    "1. 手動執行前述的資料裁剪工作或是從[Google-drive](https://drive.google.com/file/d/1M4lHt-ASGymJf-dBwRz7VmZ8sqMf6MKx/view?usp=sharing)下載預先裁剪的結果的壓縮檔\"cards.zip\"。\n",
    "2. 在這個Jupyter Notebook所在的目錄下產生一個新的子目錄\"data\"。\n",
    "3. 解壓縮圖像資料檔到\"data\"的目錄裡頭。\n",
    "4. 在\"data\"的目錄中, 產生一個子目錄\"train\"用來存放要用來訓練用的圖像檔。\n",
    "  \n",
    "最後你的目錄結構看起來像這樣:\n",
    "```\n",
    "xxxx.ipynb\n",
    "cards_data.xlsx\n",
    "data/\n",
    "├── origin_imgs/\n",
    "│   ├── c01.PNG\n",
    "│   ├── c02.PNG\n",
    "│   ├── ..\n",
    "│   └── s13.PNG\n",
    "└── train/\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 撲克牌圖像辨識 (Poker cards Recognition)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# 匯入相關所需的模組\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import glob\n",
    "from pathlib import PurePath\n",
    "import cv2\n",
    "\n",
    "import keras\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.callbacks import LearningRateScheduler, ModelCheckpoint\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.optimizers import SGD, Adam\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "# 專案的根目錄路徑\n",
    "ROOT_DIR = os.getcwd()\n",
    "\n",
    "# 置放資料的路徑\n",
    "DATA_PATH = os.path.join(ROOT_DIR, \"data\")\n",
    "\n",
    "# 置放原始圖像檔案的路徑\n",
    "ORIGIN_IMG_PATH = os.path.join(DATA_PATH, \"origin_imgs\")\n",
    "\n",
    "# 置放要用來訓練用圖像檔案的路徑\n",
    "TRAIN_IMG_PATH = os.path.join(DATA_PATH, \"train\")\n",
    "\n",
    "# 訓練用的圖像大小與色階\n",
    "IMG_HEIGHT = 28\n",
    "IMG_WIDTH = 28\n",
    "IMG_CHANNEL = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 資料預處理 (Data Preprocessing)\n",
    "預處理圖片的第一步將數據資料轉換型別為`float32`來節省一些記憶的用量並對它們進行歸一化（除以255）。\n",
    "\n",
    "然後使用one-hot編碼來將標籤(label)轉換為向量(vector)："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 進行圖像色階轉換及大小的修改\n",
    "\n",
    "為了能夠節省要訓練的參數與運算量, 我們要將圖像由彩色轉為灰階圖像, 並且將圖像的大小轉換成28x28。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 取得原始圖像的檔案路徑\n",
    "all_img_paths = glob.glob(os.path.join(ORIGIN_IMG_PATH, \"*.PNG\"))\n",
    "\n",
    "# 進行圖像色階轉換及大小的修改\n",
    "for img_path in all_img_paths:\n",
    "    filename = (PurePath(img_path).stem) # 取得原圖像檔案名稱, 例如.c01\n",
    "    new_filename = filename + \".png\" # 更換附檔名    \n",
    "    card_img_grey = cv2.imread(img_path, 0) # 使用OpenCV以灰階讀入\n",
    "    card_img = cv2.resize(card_img_grey, (IMG_HEIGHT, IMG_WIDTH)) # 轉換大小  \n",
    "    cv2.imwrite(os.path.join(TRAIN_IMG_PATH, new_filename), card_img) # 寫出"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 轉換前與轉換後的圖像資料\n",
    "轉換前的圖像:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UUMbIjiM8CkNljPkZd0x6sSTIZDO46/d3Fx8Jdj/tbADAjjvtpNV+G4o3q/Mo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\neMipAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b2ee198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8)) # 設定每個圖像顯示的大小\n",
    "\n",
    "# 產生一個3x2網格的組合圖像\n",
    "for i in range(0, 6):\n",
    "    img_file = 'h0'+str(i+1)+'.PNG'\n",
    "    img = cv2.imread(os.path.join(ORIGIN_IMG_PATH, img_file))\n",
    "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    \n",
    "    plt.subplot(330+1+i) # (331) -> 第一個子圖像, (332) -> 第二個子圖像\n",
    "    plt.title(img_file)  # 秀出原圖像的檔名\n",
    "    plt.axis('off')      #不顯示坐標\n",
    "    plt.imshow(img)      # 圖像顯示\n",
    "    \n",
    "# 展現出圖像\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "轉換後的圖像:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x4cd8e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8)) # 設定每個圖像顯示的大小\n",
    "\n",
    "# 產生一個3x2網格的組合圖像\n",
    "for i in range(0, 6):\n",
    "    img_file = 'h0'+str(i+1)+'.png'\n",
    "    img = cv2.imread(os.path.join(TRAIN_IMG_PATH, img_file),0)\n",
    "    \n",
    "    plt.subplot(330+1+i) # (331) -> 第一個子圖像, (332) -> 第二個子圖像\n",
    "    plt.title(img_file)  # 秀出原圖像的檔名\n",
    "    plt.axis('off')      #不顯示坐標\n",
    "    plt.imshow(img, cmap=plt.get_cmap('gray'))      # 圖像顯示\n",
    "    \n",
    "# 展現出圖像\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "取得要進行訓練的X與標籤y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: (52, 28, 28, 1)\n",
      "y_train: (52, 52)\n"
     ]
    }
   ],
   "source": [
    "num_classes = 52 # 撲克牌的標籤總共有52類\n",
    "img_rows, img_cols, img_channels = IMG_HEIGHT, IMG_WIDTH, IMG_CHANNEL # 圖像是 49像素 x 33像素 (灰色階: 1)\n",
    "input_shape = (img_rows, img_cols, img_channels) # (圖像的height, 圖像的width, 圖像的顏色通道數channel)\n",
    "\n",
    "# 載入標籤資料檔\n",
    "cards_data = pd.read_excel(os.path.join(DATA_PATH, 'cards_data.xlsx'))\n",
    "\n",
    "# 取得\"card_label\"的欄位資料\n",
    "cards_label = cards_data['card_label']\n",
    " \n",
    "# 產生相關的查找的字典物件\n",
    "idx_to_label = {k:v for k, v in cards_label.iteritems()}\n",
    "label_to_idx = {v:k for k, v in cards_label.iteritems()}\n",
    "\n",
    "# 取得所有圖像的標籤值\n",
    "y = np.array(cards_label.index.values)\n",
    "\n",
    "# 進行標籤的one-hot編碼\n",
    "y_train = to_categorical(y, num_classes)\n",
    "y_test = y_train.copy()\n",
    "\n",
    "# 將每個圖像從檔案中讀取進來\n",
    "imgs = []\n",
    "all_img_paths = glob.glob(os.path.join(TRAIN_IMG_PATH, \"*.png\"))\n",
    "\n",
    "#進行圖像每個像素值的型別轉換與歸一化處理\n",
    "for img_path in all_img_paths:\n",
    "    img = cv2.imread(img_path,0) # 以灰階讀入\n",
    "    img = img.astype('float32')/255.\n",
    "    imgs.append(img)\n",
    "    \n",
    "# 取得要進行訓練用的灰階圖像\n",
    "X = np.array(imgs)    \n",
    "\n",
    "# 將圖像數據集的維度進行改變 \n",
    "# 改變前: [樣本數, 圖像寬, 圖像高] -> 改變後: [樣本數, 圖像寬, 圖像高, 圖像頻道數]\n",
    "X_train = X.reshape(X.shape[0], 28, 28, 1)\n",
    "X_test = X_train.copy()\n",
    "\n",
    "print(\"X_train:\", X_train.shape)\n",
    "print(\"y_train:\", y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 網絡模型 (Model)\n",
    "\n",
    "現在我們來定義我們的模型架構。我們將使用具有6個卷積層的前饋網絡，然後是完全連接的隱藏層。\n",
    "我們也將在兩者之間使用Dropout層來防止網絡\"過擬合(overfitting)\"。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 28, 28, 32)        320       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 26, 26, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 13, 13, 64)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 13, 13, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 6, 6, 128)         73856     \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 6, 6, 128)         147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 3, 3, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 3, 3, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 1152)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               590336    \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 52)                26676     \n",
      "=================================================================\n",
      "Total params: 903,444\n",
      "Trainable params: 903,444\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 產生一個Keras序貫模型\n",
    "def cnn_model():\n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=input_shape))    \n",
    "    model.add(Conv2D(32, (3, 3), activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2\n",
    "                                     )))\n",
    "    model.add(Dropout(0.2))\n",
    "    \n",
    "    model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
    "    model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.2))\n",
    "    \n",
    "    model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
    "    model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.2))\n",
    "    \n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(512, activation='relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "    \n",
    "    return model;   \n",
    "\n",
    "model = cnn_model() # 初始化一個模型\n",
    "model.summary() # 秀出模型架構"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在訓練模型之前，我們需要將模型配置為學習算法並進行編譯。我們需要指定:\n",
    "* `loss`: 損失函數，我們要優化。我們不能使用`MSE`，因為它是不連續的數值。因此，我們使用：`categorical_crossentropy`\n",
    "* `optimizer`: 我們使用標準隨機梯度下降(Stochastic gradient descent)與涅斯捷羅夫動量(Nesterov momentum)\n",
    "* `metric`: 由於我們正在處理一個分類問題，我們用度量是`accuracy`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 讓我們先配置一個常用的組合來作為後續優化的基準點\n",
    "lr = 0.01\n",
    "sgd = SGD(lr=lr, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "             optimizer=sgd,\n",
    "             metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 圖像增強 (Data Augmentation)\n",
    "\n",
    "因為我們只有52張圖像, 因此利用現有的圖像來生成新的訓練圖像，這將是一個很好的方式來增加訓練數據集的大小。\n",
    "\n",
    "讓我們直接使用keras的內置功能來完成圖像增強 (Data Augmentation)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datagen_train = ImageDataGenerator(rotation_range=3.)\n",
    "datagen_train.fit(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 訓練 (Training)\n",
    "\n",
    "現在，我們的模型已經準備好了。在訓練期間，我們的模型將進行迭代批量訓練，每個次的訓練資料的大小為batch_size。對於每批次，模型將會計算出梯度(gradient)，並自動更新網絡的權重。對所有訓練集的一次迭代被稱為一次的循環(epoch)。訓練通常會一直進行到損失收斂於一個常數。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/700\n",
      "    1/50000 [..............................] - ETA: 24:08:18 - loss: 3.9520 - acc: 0.0192Epoch 2/700\n",
      "    1/50000 [..............................] - ETA: 5:39:59 - loss: 3.9535 - acc: 0.0385Epoch 3/700\n",
      "    1/50000 [..............................] - ETA: 5:52:29 - loss: 3.9386 - acc: 0.0385Epoch 4/700\n",
      "    1/50000 [..............................] - ETA: 6:38:19 - loss: 3.9512 - acc: 0.0000e+00Epoch 5/700\n",
      "    1/50000 [..............................] - ETA: 5:47:29 - loss: 3.9392 - acc: 0.0577Epoch 6/700\n",
      "    .....\n",
      "    .....\n",
      "    .....\n",
      "    1/50000 [..............................] - ETA: 5:26:39 - loss: 0.0091 - acc: 1.0000Epoch 688/700\n",
      "    1/50000 [..............................] - ETA: 5:33:19 - loss: 0.0129 - acc: 1.0000Epoch 689/700\n",
      "    1/50000 [..............................] - ETA: 5:29:09 - loss: 0.0187 - acc: 1.0000Epoch 690/700\n",
      "    1/50000 [..............................] - ETA: 5:31:39 - loss: 0.0268 - acc: 1.0000Epoch 691/700\n",
      "    1/50000 [..............................] - ETA: 5:43:19 - loss: 0.0354 - acc: 0.9808Epoch 692/700\n",
      "    1/50000 [..............................] - ETA: 5:24:59 - loss: 0.0157 - acc: 1.0000Epoch 693/700\n",
      "    1/50000 [..............................] - ETA: 5:20:49 - loss: 0.0060 - acc: 1.0000Epoch 694/700\n",
      "    1/50000 [..............................] - ETA: 5:21:39 - loss: 0.0054 - acc: 1.0000Epoch 695/700\n",
      "    1/50000 [..............................] - ETA: 5:26:39 - loss: 0.0188 - acc: 1.0000Epoch 696/700\n",
      "    1/50000 [..............................] - ETA: 5:24:59 - loss: 0.0150 - acc: 1.0000Epoch 697/700\n",
      "    1/50000 [..............................] - ETA: 5:23:19 - loss: 0.0089 - acc: 1.0000Epoch 698/700\n",
      "    1/50000 [..............................] - ETA: 5:26:39 - loss: 0.0421 - acc: 0.9808Epoch 699/700\n",
      "    1/50000 [..............................] - ETA: 5:28:19 - loss: 0.0086 - acc: 1.0000Epoch 700/700\n",
      "    1/50000 [..............................] - ETA: 5:58:19 - loss: 0.0055 - acc: 1.0000"
     ]
    }
   ],
   "source": [
    "batch_size = 100\n",
    "steps_per_epoch = 50000\n",
    "training_epochs = 700\n",
    "\n",
    "# 透過data generator來產生訓練資料, 由於資料是可持續產生, 我們可以透過設定'steps_per_epoch'的數量來讓模型可以有更多的訓練批次\n",
    "history = model.fit_generator(datagen_train.flow(X_train, y_train, batch_size=batch_size),\n",
    "                            steps_per_epoch= steps_per_epoch,\n",
    "                            epochs=training_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 訓練過程的可視化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X5bW+ThkbUXswoO7B9lY1oLzWj7c3lGF4QXZkv/Fmk8wiMERERLGOZ8Xd0f1z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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1baa80f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 透過趨勢圖來觀察訓練與驗證的走向 (特別去觀察是否有\"過擬合(overfitting)\"的現象)\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_train_history(history, train_metrics):\n",
    "    plt.plot(history.history.get(train_metrics))\n",
    "    plt.ylabel(train_metrics)\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.legend(['train'])\n",
    "    \n",
    "    \n",
    "plt.figure(figsize=(12,4))\n",
    "plt.subplot(1,2,1)\n",
    "plot_train_history(history, 'loss')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plot_train_history(history, 'acc')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 驗證評估 (Evaluation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52/52 [==============================] - 0s 3ms/step\n",
      "Test loss: 0.000443235971873\n",
      "Test accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_train, y_train, verbose=1)\n",
    "\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在這個範例中, 由於圖像資料太少所以並沒有特別在驗證資料集去進行驗證。但是從訓練資料集的檢查來看,這個卷積網路模型的確可以有效地識別每一張撲克牌圖像。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 每一張撲克牌的預測\n",
    "我們再來看每一張圖像的驗證結果:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 打散圖像集的順序\n",
    "randomize = np.arange(len(X_test))\n",
    "np.random.shuffle(randomize)\n",
    "X_test_randomize = X_test[randomize]\n",
    "y_test_randomize = y_test[randomize]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52/52 [==============================] - 0s 3ms/step\n",
      "Test loss: 0.000443235991182\n",
      "Test accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "# 計算打散後的圖像集驗證\n",
    "score = model.evaluate(X_test_randomize, y_test_randomize, verbose=1)\n",
    "\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t[Info] Making prediction of X_test_randomize\n",
      "\n",
      "\t[Info] Show 52 prediction result (From 0):\n",
      "[ 9  6  7 30  8 21  3 14 31 26 35 12 34 17 50 15 37 44 23 43 51  2  5 49 24\n",
      " 20 42 10 25 46 33 38 29  4 13 11 27 19 45 32 48 47  0 28  1 22 16 39 40 18\n",
      " 36 41]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 進行每一張牌的圖像辨識預測\n",
    "print(\"\\t[Info] Making prediction of X_test_randomize\")  \n",
    "prediction = model.predict_classes(X_test_randomize)  # Making prediction and save result to prediction  \n",
    "\n",
    "print()  \n",
    "print(\"\\t[Info] Show 52 prediction result (From 0):\")  \n",
    "print(\"%s\\n\" % (prediction[:52]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c630eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ba9d4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1be287f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bd9c630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bdd2278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c47f9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c68ddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bd937f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c67c588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c5c3ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d72b4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dbdb630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dd2afd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dbb5fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d758240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dd90400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dbb52e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dd5df28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ba810b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ba5b978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ba74588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ddd5dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c6d96d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d725f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c6d2fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d76d748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1de27160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c462e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bd3be10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c55bc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c535080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c458550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bda2a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c69d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c4a62e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b7359b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ba56278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c687b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c4790b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bcddf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bce2fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bd49d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c6b8160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dbd5ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dd30e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d795358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bdf46a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d76e978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d733c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c4930b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1de0c048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1de10f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 把每一張牌的圖像及預測結果打印出來\n",
    "for i in range(X_test_randomize.shape[0]):\n",
    "    true_class = idx_to_label.get(np.argmax(y_test_randomize[i]))\n",
    "    predict_class = idx_to_label.get(prediction[i])\n",
    "    plt.title(\"Predicted:[{}], True:[{}]\".format(predict_class, true_class))\n",
    "    plt.imshow(X_test_randomize[i].reshape(28,28), cmap=plt.get_cmap('gray'))\n",
    "    plt.tight_layout()  \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 總結 (Conclusion)\n",
    "\n",
    "在這篇文章中有一些個人學習到的一些有趣的重點:\n",
    "* 透過土炮自製的圖像來進行深度學習,不僅有趣同時學習起來更有效率\n",
    "* 當圖像資料不多的時候, 圖像增強 (Data Augmentation)可以幫上忙"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
